refactor: screen() reuses run_combination(), single runner across combo sizes

- screen() now calls run_combination() internally, eliminating duplicated
  signal extraction/combination logic
- _run_combo_screen creates one CombinationRunner before the size loop,
  so signal cache is reused across 2-factor and 3-factor screens
- Add MAJORITY(2)=AND note to screen() docstring

run_all_strategies.py

@@ -97,19 +97,20 @@ def _run_combo_screen(
 
     from math import comb
 
+    # 单个 Runner 跨 size 复用信号缓存，避免重复提取
+    runner = CombinationRunner(
+        strategy_classes=strategy_classes,
+        df=df,
+        cash=cash,
+        commission=commission,
+    )
+
     for size in combo_sizes:
         total = comb(len(strategy_classes), size)
         click.echo("\n" + "=" * 80)
         click.echo(f"[*] {size}因子组合回测 (共{total}组, 模式={combo_mode})")
         click.echo("=" * 80)
 
-        runner = CombinationRunner(
-            strategy_classes=strategy_classes,
-            df=df,
-            cash=cash,
-            commission=commission,
-        )
-
         results = runner.screen(combo_sizes=(size,), mode=combo_mode.upper())
 
         if not results:

src/easy_tdx/backtest/combo.py

@@ -398,6 +398,8 @@ class CombinationRunner:
         Args:
             combo_sizes: 要尝试的组合大小（如 (2, 3) 表示 2 因子和 3 因子组合）
             mode: 信号合并模式（AND / OR / MAJORITY）
+                注意：MAJORITY 模式下 2 因子需要两个都同意（等同 AND），
+                因为阈值 = 2/2 = 1.0，需 > 1.0 才触发。
             filter_zero_trades: 是否过滤零交易组合
             top_n: 只返回前 N 名（0 = 全部返回）
 
@@ -412,16 +414,9 @@ class CombinationRunner:
                 continue
 
             for combo in itertools.combinations(range(n_factors), size):
+                result = self.run_combination(combo, mode=mode)
+
                 signals = [self._get_factor_signals(i) for i in combo]
-
-                # 合并信号
-                buy_mask, sell_mask = combine_masks(signals, mode=mode)
-
-                # 包装为策略并运行
-                combo_cls = _make_combo_strategy(buy_mask, sell_mask)
-                engine = self._make_engine(combo_cls)
-                result = engine.run(self._df)
-
                 name = " + ".join(s.name for s in signals)
 
                 results.append(